To reduce this sim-to-real gap even further, a large amount of synthetic scene generation with wide variability is required. Creating a large number of synthetic scenes suffers from either manually creating high-quality 3D assets or bringing together these assets in a structured way to form a scene. Thus, a scalable simulation environment becomes a necessity for neural networks to improve the quality of the prediction models. [1]} and [2]} REF  demonstrated the power of scalable data by providing an in-depth analysis of the prediction using their data for training. In this work, we present a scalable framework to automatically generate structured and unstructured environments for perception and robotics applications without any need for manual effort. Fig. REF  demonstrates the ability of WorldGen to autonomously generate structured cities, object datasets and object fragments without any requirement for manual efforts.
